Strategies for the development of the State of Acre in
Brazil: a spatial approach
1Assistant Professor of Economics - UFPE and PhD in Economics by UFPE. Correo electrónico:
denis_fernandes@outlook.com. Orcid: https://orcid.org/0000-0001-6229-5973.
Estratégias para o desenvolvimento do Estado do Acre no Brasil: uma
abordagem espacial
D e n i s F e r n a n d e s A l v e s 1
Submission date: October 15, 2024
Approval date: November 5, 2024
Abstract
This study aims to analyze the productive structure and the determinants of intermunicipal
income inequalities in the state of Acre, Brazil. Regional economic indicators and Spatial
Econometrics techniques were used. The guiding hypothesis is that the dynamics of the
labor market influenced Acre's economic development strategy. The results reveal the
dynamics of the trade and services sectors, which strengthened the economy during the
COVID-19 pandemic, and the agricultural sector, which saw a decrease in its relative
share, followed by construction and industry. In addition to reinforcing public and social
assistance policies, the study concludes that there were spatial effects from health,
education, infrastructure, and other variables on the per capita income differentials among
the municipalities.
Keywords: Acre; Rent inequality; Productive Structure; Regional Economy; Spatial
econometrics.
JEL rating: C31; R1; O1.
Resumo
O estudo se propõe a analisar a estrutura produtiva e os determinantes das desigualdades
intermunicipais de renda no estado do Acre, Brasil. Foram utilizados indicadores da
economia regional e técnicas de Econometria Espacial. A hipótese norteadora é a de que
a dinâmica no mercado de trabalho afetou a estratégia de desenvolvimento econômico do
Acre. Os resultados mostram a dinâmica dos setores de comércio e serviços que
fortaleceram a economia no contexto da pandemia da COVID-19 e o setor agropecuário
que teve redução de participação relativa, seguido da construção civil e indústria. Além
de reforçar as políticas públicas e assistencialistas, o estudo conclui que houve efeitos
espaciais da saúde, educação, infraestrutura e outras variáveis sobre os diferenciais de
renda per capita entre os municípios.
Palavras-chave: Acre; Desigualdade de Renda; Estrutura Produtiva; Economia
Regional; Econometria espacial.
Classificação JEL: C31; R1; O1
1. Introduction
The State of Acre has historically been recognized as the largest rubber producer in Brazil
and the world. Located in the northern macro-region of the country, it is one of the least
populous and most economically isolated states. Rubber, often referred to as “black gold,”
emerged as an icon of the industrial revolution, primarily concentrated in the Amazon
rainforest (Souza, 1992).
The occupation of the Acre region began in the late 1870s during the rubber boom in the
Amazon. This process of regional settlement was shaped by various factors. On one hand,
it was influenced by local societal conditions; on the other hand, it was driven by the
transformation of production into a branch of scientific activity— a trend characteristic
of central economies during periods dominated by monopolistic capital (Lima, 1994).
As rubber prices soared in the markets of Belém, Manaus, and internationally, there was
a significant increase in rubber exploitation. By 1880, the expansion of rubber extraction
had begun in Acre; between 1880 and 1883, all the rivers of Acre were already occupied,
and the area was populated by Brazilians. Thus, it can be concluded that the rubber cycle
not only shaped the economy of Acre but also facilitated settlement and attracted migrants
to the region.
The economy of Acre is primarily based on extractive activities, particularly rubber
exploitation, which was fundamental to the region's settlement. Currently, timber is the
state’s main export, while it is also a significant producer of Brazil nuts, which are widely
exported (Martinello, 1988).
Acre features two major economic centers: the Juruá River valley, with the city of
Cruzeiro do Sul as its main urban nucleus, and the more industrialized Acre River valley.
The latter exhibits a higher degree of mechanization and modernization in agriculture and
is a major producer of rubber and various food crops (such as cassava, rice, corn, and
fruits), in addition to housing the state capital, Rio Branco. The Acre River valley plays a
significant role in the agricultural sector and has undergone substantial modernization,
placing it at a more advanced stage of development compared to the Juruá River valley.
Due to its rubber industry and the significant influence of extractivism, Acre is home to
Brazil's largest extractive reserve, the Chico Mendes Reserve. This reserve aims to
preserve the area and sustain local communities through the resources provided by the
forest. However, activities that deviate from these objectives, such as illegal logging and
improper resource extraction, have had a marked impact on environmental degradation
that persists to this day. The reserve encompasses six municipalities in Acre, including
Xapuri, Brasiléia, Rio Branco, Assis Brasil, Capixaba, and Sena Madureira.
Overall, the Acre economy is characterized by its primary sector, which includes
agriculture, livestock, and extractivism. As with many less industrialized and polarized
economies, these activities generally require lower levels of qualification. Acre also has
a strong tertiary sector, encompassing trade and services, which are crucial for generating
wealth and employment in the region.
In terms of foreign trade, alongside products such as wood and nuts, agribusiness
development has been gaining traction, supported by government initiatives to promote
soybean production for export. However, in the secondary sector, industries in Acre
contribute only 1.5% of regional industrial output, placing it just ahead of Roraima, which
accounts for 1.2% (Fernandes & Bezerra, 2019). In addition to extractivism, Acre engages
in other economic activities, such as livestock and agriculture, being one of the states that
has successfully eradicated foot-and-mouth disease, which has facilitated meat exports,
especially beef. Another notable export is birds from the city of Brasiléia.
The state of Acre has a range of geographical, climatic, and logistical characteristics that
highlight inequalities across its territory. The polarization of the territory and access to
certain regions present ongoing challenges related to regional imbalances. Over recent
decades, these disparities have remained significant across various dimensions, including
income, wages, employment, and production, as well as social indicators.
Despite economic growth in Acre in recent years, driven by the region's territorial and
urban potential, significant domestic inequalities, particularly regarding income, still need
to be addressed. These inequalities are rooted in historically established economic and
social advantages that favor some regions over others (Silva et al., 2010).
Consequently, analyses that incorporate spatial factors to study income inequality in the
region become increasingly nuanced. It is essential to investigate the primary economic
variables that contribute to reducing intermunicipal income disparities, as well as their
direct, indirect, and total impacts on the economy. Furthermore, regional indicators can
provide insights into the characteristics of Acre's productive structure. The relative
participation of various activities within each region influences the nature of regional
development. In many instances, a region may be considered specialized in a particular
activity, reflecting the impact of specific sectors on the distribution of formal employment
in relation to the total workforce in the state.
Additionally, some measures of specialization aim to compare the productive structure of
a microregion with that of the state. A region exhibiting a more distinct productive
structure compared to the state will be deemed specialized. Conversely, restructuring
coefficients can indicate whether changes in the productive structure have occurred over
time.
Understanding these characteristics of the Acre economy through established regional
indicators reveals how, when, and where sectoral development has advanced in certain
areas of the state in recent years, as well as the types of policies or economic conditions
that have facilitated this progress. It also helps identify the most dynamic sectors and how
they perform during times of crisis.
Other studies also highlight intermunicipal income inequalities in Brazil. Alves (2023a),
in analyzing the state of São Paulo, identified significant disparities between its
mesoregions, despite being one of the most developed regions. Between 2015 and 2020,
more urbanized areas expanded sectors such as services and industry, while others faced
economic downturns. The more developed regions, especially during the 2020 pandemic,
stood out for their strong performance, with a focus on the services sector. The
inequalities are concentrated in specific clusters and directly affect areas such as health,
education, and infrastructure, emphasizing the need for public policies to reduce these
disparities. Alves (2023b) analyzed the state of Rio Grande do Norte and concluded that
the Leste Potiguar mesoregion is the most developed and employs the most people in the
state. The study also highlighted the importance of the public sector in small towns to
mitigate inequalities, as well as the need for improvements in water supply, energy
provision, education, and healthcare.
In this context, this study analyzes the dynamics of the productive structure in Acre’s
microregions and the determinants of intermunicipal income inequalities, focusing
particularly on spatial factors. The specific objectives include: i) exploring the sectors of
Acre's economic structure; ii) conducting an exploratory spatial analysis of per capita
income among the state's municipalities; iii) understanding the structural polarization
between the two mesoregions; and iv) analyzing intermunicipal income inequalities. The
study also seeks to answer questions such as: What are the most dynamic sectors of the
economic structure? Is the spatial factor relevant in explaining inequalities? What are the
impacts?
The study relies on two main hypotheses: first, that the productive structure reflects the
process of territorial development, highlighting disparities and similarities among Acre’s
microregions; and second, that spatial factors explain differences in per capita income
across municipalities in Acre, based on socioeconomic indicators such as income
distribution, health, education, and infrastructure.
This study significantly contributes to the field by using established indicators of regional
economics and conducting an econometric analysis that incorporates a temporal panel
and spatial autocorrelation. By integrating these elements and examining Acre's sectoral
productive structure, the study reveals the direct, indirect, and total impacts of
intermunicipal per capita income differentials, providing valuable insights for discussion.
Following this introductory section, the study will cover aspects of Acre’s economy,
followed by methodology, results, conclusions, and future perspectives on the state’s
economy.
2. Background
2.1. Economy of Acre: sectors of the productive structure and its characteristics in
the microregions
The North region of Brazil has historically faced significant challenges regarding
development and integration into the national market. Various factors have contributed to
the interregional inequalities (Fishlow, 1973), including climatic, environmental,
political, infrastructure, logistical, economic, and social aspects of this vast and dispersed
area covered by the Amazon rainforest. Economic transformations in the North, primarily
driven by state action, did not lead to uniform regional development as anticipated. States
such as Acre, Amapá, Roraima, and Tocantins, characterized by limited economic
dynamism, have had to contend with the persistent issue of intraregional inequalities.
2.2 Historical Aspects of Acre's Economy
During the 1990s and the early 2000s, the forest played a crucial role in supporting the
Acrean economy, making the plant extraction industry the primary livelihood for the
population. Today, while the economy still relies on rubber and Brazil nut extraction,
along with livestock activities, trade and services have become the flagship sectors.
Culturally, trade continues to be influenced by river routes, with exported products
primarily directed towards the states of Amazonas and Pará.
Acre stands out in rubber production, with rubber trees thriving in the Purus, Juruá, and
Madeira River basins. The collection of Brazil nuts is also a key activity, typically carried
out by rubber tappers as a supplementary occupation during the rainy season, although
this crop is not produced regularly. Subsistence agriculture is practiced, with important
crops such as cassava, rice, bananas, and corn also contributing to the state's economy
and the livelihood of its residents.
Territorial and demographic changes reflect a process of urbanization and population
growth, particularly in Rio Branco, which accounts for 80% of the state's GDP from
industry and services. This concentration leads to significant disparities across economic
sectors and regions with higher economic participation. The Gini index, a measure of
inequality, highlights these differences (Neves, 2015).
In the early 21st century, Acre exhibited a high Gini index, indicating concentrated
economic polarization, particularly in the microregions of Cruzeiro do Sul and Rio
Branco, which hold a substantial portion of the state GDP. However, disparities have
decreased over the years, as reflected by the Gini index, which was 0.69 in 2020. The
inequalities within the agricultural sector's value-added production are relatively low, as
the state has a strong presence in this segment. In contrast, industrial activities are
concentrated in the capital, Rio Branco, and its surroundings. Like other regions, the
service sector has seen a reduction in inequalities, leading to a decline in the Gini index
for this sector. Overall, while agriculture and services have recently shown a decrease in
inequality, the industrial sector experienced increased inequality between 2000 and 2010
but saw a reduction by 2020.
Table 01: Gini Index of Acre's participation in the productive structure (%)
Acre
Gini Index
GDP
VAB
Agricultural
Industry
Services
2000
0.70
0.45
0.80
0.73
2010
0.72
0.32
0.86
0.73
2020
0.69
0.31
0.82
0.72
Source: Own elaboration based on regional accounts - IBGE (2024).
In 2010, Acre's Gross Domestic Product (GDP) reached R$ 8.3 million, accounting for
0.21% of Brazil's GDP and 4.03% of the Northern region's GDP. It is important to note
that from 2003 to 2010, both national and regional economies experienced growth.
Between 2013 and 2014, the state saw substantial growth, with a GDP growth rate of
4.4%. This period coincided with a favorable national economic environment, even in the
aftermath of the 2008 financial crisis, creating a more favorable climate for job creation
1
.
Graph 01: Formal employment in Acre - 2010-2020
Source: own elaboration from RAIS/MTE data.
Over the years, formal employment in Acre has shown a growth trend, although it
experienced declines between 2015 and 2016, as well as from 2017 to 2019, when it
reached its lowest levels. In 2020, the trade and services sectors became significant
contributors to job creation, particularly because Acre is not heavily industrialized. As a
1
The 2007–2008 financial crisis is so named after the global economic situation affected by the international financial
crisis and precipitated by the bankruptcy of investment bank Lehman Brothers in the United States, it is about a
traditional bench.
121.187
121.321
125.229
129.232
133.161
136.011
128.137
131.291
126.304 125.272
132.851
110.000
115.000
120.000
125.000
130.000
135.000
140.000
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Acre
result, the impact of the COVID-19 pandemic on the industrial sector was relatively
minimal.
2.3 Characteristics of Acre's Microregions
Acre features a humid and hot climate in the extreme west of Brazil, covering an area of
153,000 km² that includes a mix of small towns and Amazonian cities. The state
comprises twenty-two municipalities, five microregions, and two mesoregions. The Acre
River is vital to both the life and economy of the region.
Figure 01 illustrates the state’s five microregions, highlighting aspects related to
waterways, railways, and aerodromes. All microregions are intersected by rivers, whether
navigable or not, and are connected by an extensive railway that runs from east to west.
The hydrographic basin of the State of Acre is part of the larger Amazon basin. Among
its major rivers are the Juruá, Tarauacá, Muru, Embirá, Xapuri, Purus, Iaco, and Acre
Rivers.
Figure 01: Microregions of Acre
Source: elaboration of the author.
In terms of formal employment, most jobs are concentrated in the microregion of Rio
Branco, which particularly reflects Acre's economic cycle, as illustrated in Graph 01.
However, it is important to highlight the growth in formal job opportunities in the other
microregions. Although this growth occurs at a slower pace, the increase in formal
employment has been consistent, regardless of the prevailing economic conditions.
Graph 02: Total formal labor of the productive structure by microregion of Acre -
2010/2020
Source: own elaboration from RAIS/MTE data.
In 2010, in terms of GDP, the microregion of Rio Branco led with R$ 5.9 million,
followed by Cruzeiro do Sul with R$ 1.2 million, Brasiléia with R$ 495,900, Tarauacá
with R$ 470,000, and Sena Madureira with R$ 356,000. These figures highlight the
marked polarization within the state, with Rio Branco and Cruzeiro do Sul as the two
cities exhibiting significant economic dynamism. Together, their microregions account
for a substantial portion of the state's GDP (Pessôa, 1997).
Regarding Graph 03, it is noteworthy that despite 2020 being a challenging year for job
and income generation globally due to the pandemic, the only microregion that
experienced a decline in the number of formal employees was Rio Branco. In contrast,
all other microregions showed growth in the number of occupied positions.
0
2000
4000
6000
8000
10000
12000
14000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
CRUZEIRO DO SUL
0
1000
2000
3000
4000
5000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
TA R A UACÁ
0
500
1000
1500
2000
2500
3000
3500
4000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
SENA M A DUREIR A
0
1000
2000
3000
4000
5000
6000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
B RA S ILÉIA
90000
95000
100000
105000
110000
115000
RIO BRA N CO
Graph 03: Formal labor by microregions of Acre - 2015/2020
Source: own elaboration from RAIS/MTE data.
As expected, the concentration of economic activity in the microregion of Rio Branco is
linked to the population density of the capital and its neighboring areas, which hold the
largest share of formal jobs in the state. This microregion was notably affected by the
pandemic. The capital possesses unique characteristics stemming from its historical,
economic, and geographical formation.
Geographically, the municipality of Rio Branco covers an area of 14,294 km² along the
banks of the Acre River, with an elevation of 152.5 meters. Historically, the city originated
from the Volta de Empresa colonization nucleus, established in 1882 during the rubber
boom in the Amazon. Twenty years later, it became the headquarters for the Brazilian
forces led by Plácido de Castro, who opposed Bolivian control in the region. After Acre's
incorporation into Brazil in 1903, the settlement was granted city status. It was named
Rio Branco in 1912 in honor of the Baron of Rio Branco, who negotiated the Treaty of
Petrópolis, signed on November 17, 1903, which delineated the border between Brazil
and Bolivia.
At the heart of Rio Branco lies the Rubber Museum, which houses collections of historical
and ethnographic significance for both tourists and scholars, contributing to the city’s
cultural heritage and economic vitality.
3. Empirical strategy
As an empirical strategy, indicators of the regional economy and spatial econometry
techniques applied to the state of Acre were used. The data come from secondary sources
in official bodies such as RAIS, IBGE, and UNDP (Demographic Census, UNDP Human
Development Atlas, and DATASUS, among others). The time frame runs from 2000 to
2020. The regional economy indicators used were relative participation, three location
measures: Location Quotient, Location Coefficient, and Hirschman-Herfindahl Index
11.229
4.028 3.313
112.856
4.585
12.243
4.266 3.364
107.742
5.236
0
20.000
40.000
60.000
80.000
100.000
120.000
Cruzeiro do Sul Tarauacá Sena Madureira Rio Branco Brasiléia
2015 2020
(HHI), and two specialization measures: Specialization Coefficient (SC) and
Restructuring Coefficient (RC) (Haddad, 1989).
After the analysis variables are chosen, the following definitions should be taken into
account: = employed persons in the sector i of the region j; = total number of
people employed in the region j; = employed persons, from the sector i in the
reference region; = total number of persons employed in the reference region;
= distribution of the sector i between regions;
=distribution i in the region
j, that is, shows the participation of each sector in the productive structure of each micro-
region; = initial year; = final year. This index examines the expression PR, defined
as:
(1)
The relative participation indicator refers to the employed population, by sector, in each
of the micro-regions (Piacenti y Lima, 2017; Rocha y Parré, 2009). This index varies
between zero and one, and it is possible to highlight that at least two large sectors, in each
micro-region, have greater participation in the economy, they are: Commerce and
Services. The formula for calculating QL is expressed as:
(2)
This indicator shows which sectors are more specialized in certain regions in relation to
a reference macro-region. In this sense, a QL above one (1) indicates that the region is
considered specialized in a certain sector (Castro et al., 2017). The calculation of
utilizes the following formula:
(3)
It highlights the influence of a given sector in relation to formal workers employed in a
given micro-region, considering the total workforce in Acre. In this way, it allows
identifying the degree of relative dispersion of economic activities and selecting those
that would have a lower tendency to spatial concentration. Following a specific
expression denoted as IHH, the formula:
(3)
The Herfindahl-Hirschman index aims to highlight the concentration of a certain sector
in the micro-region compared to Acre.
Two indicators of specialization measures were used, the Specialization Coefficient and
the Restructuring Coefficient. About the Specialization Coefficient, it aims to compare
the productive structure of the micro-region with that of Acre. In this way, the microregion
that has a more differentiated production structure compared to that of the state will be
specialized in that sector(s). For the computation of , the formula is given by:
(4)
The restructuring coefficient of the production structure. This indicator demonstrates
whether the productive structure changed between 2015 and 2020, one of the explanations
being that the micro-region began to specialize in a certain sector compared to the state's
economy. In this sense, there were changes in the productive structure of all microregions
(Simões, 2005). To interpret this information, the formula:
(5)
To analyze the dynamics of formal employment, the five sectors of the economy of the
State of Acre were used, the study uses secondary source data from the Annual Social
Information Report maintained by the Ministry of Labor (RAIS/MT), which is the main
source of formal employment data. They include the following productive activities: i)
Industry; ii) Civil Construction; iii) Trade; iv) Services and v) Agriculture. For this
purpose, data were used from formal employment links in Acre from 2010 to 2020, for
historical purposes, in addition, the study shows analyses of the years 2015 and 2020, in
these five years there are periods of reduction and increase in the number of formal jobs,
which reflect the warming and the slowdown of the Acre economy (Lima et., 2017).
For inter-municipal income inequalities, spatial economy techniques were used. To
analyze inter-municipal income inequalities, an Exploratory Spatial Data Analysis (ESA)
was performed, which is a set of tools that allows one to know spatial data and constitutes
a relevant preliminary step before performing spatial econometric exercise (Almeida,
2012).
According to Anselin (1988), Almeida (2012), and Alves (2020, 2023a, 2023b), the use
of Spatial Econometrics in regional economic analysis is grounded in the research
problem, the spillover mechanism, and indicators of spatial autocorrelation. According to
Alves (2020), intermunicipal inequalities are often serious indicators of regional
underdevelopment. Acre is one of the most isolated states in Brazil, located in the far west
of the North region, where there is a high degree of disconnection from more
economically developed areas, such as Manaus, the capital of the state of Amazonas. In
this context, identifying these inequalities within Acre’s territory is crucial for formulating
development strategies. Considering that municipalities are interconnected through
borders and economic relationships, it is believed that there is a spillover effect in the
spending behavior of formal (and informal) workers' income, influenced by the dynamics
of the labor market and the productive structure. This spillover effect will be captured by
Moran's I index, and to confirm this hypothesis, econometric models will be used, with
rigorous control of spatial effects.
The ESA was made for the variable per capita income and per capita income differentials
for the years 2000 and 2010, census data from IBGE. The idea is to analyze this variable
spatially, to understand if there is a spatial correlation and if there is cluster formation,
that is, municipalities with high (low) per capita income are surrounded by municipalities
with the same characteristics with high (low) or inversely low (high). Thus, clusters of
the High-High (AA), Low-Low (BB), High-Low (AB), and Low-High (BA) types may
exist. For this, a neighborhood matrix of the two closest neighbors was used, about a
given municipality, thus, from its centroid, the two closest municipalities are considered
borderline and, therefore, neighbors. In this analysis, the univariate and bivariate context
was verified. In the univariate case, which is about the variables of per capita income and
per capita income differentials, these were tested for spatial autocorrelation and the LISA
maps and univariate dispersion map were obtained.
(6)
As well as bivariate spatial autocorrelation tests between per capita income differentials
with infant mortality rate, low-income population, and education. Key variables of
immense importance for state and municipal public policies were selected for this
analysis.
(7)
In a second moment, a spatial regression was performed to analyze the phenomenon, that
is, the inter-municipal differences in per capita income. The Regression Model with Fixed
Effects Panel with Spatial Dependence Panel - SAR Model was estimated due to the
presence of heteroscedasticity, being corrected by the parametric method. In the SAR
model, the phenomenon to be modeled, occasionally, may have a request that the implicit
spatial dependence is more intricate, expressed in a lag of the dependent variable (Faggio
y Overman, 2014).
(8)
For the analysis of per capita income differentials, a dependent variable was created in
the model, which is the difference between the average per capita income of Acre and the
per capita income of the municipality in question (VDRENPC). This variable reflects
income disparities between the municipalities of the state, capturing the economic
variations across the Acrean territory.
Additionally, the following data (independent variables, X) will be considered: i) Theil
Index (VTHEIL): The Theil index measures how much the observed income distribution
(where each individual holds a fraction of the total income) deviates from a perfectly
uniform distribution (where each individual has an equal fraction of 1/n of the total
income). It quantifies the degree of income inequality, weighting each observation
according to its share of total income (Neri, 2010). ii) Education (VEDUC): The average
number of years of education for people aged 20 years or older. This variable is calculated
as the ratio of the total years of completed education for people in this age group to the
total number of people aged 20 or older. iii) Health (VMINF5): The infant mortality rate
under 5 years, i.e., the number of children who will not survive the first five years of life
per 1,000 live births. iv) Infrastructure: Infrastructure will be measured by two variables:
a) the percentage of people living in households with electric lighting, whether from the
public grid or not, with or without a meter (VEE); b) the percentage of people in
households with water supply (VAA). Both variables are derived from the Demographic
Census. v) Public Administration GDP (VABAdm): The Gross Added Value (VAB)
generated by the public administration sector of a municipality, used by the IBGE to
control the influences of local government on the analyzed variables. vi) Low-Income
Population (VPBRp): The percentage of the population with an income lower than half
the minimum wage. vii) Population Density (VDENSPOP): Population density is a
measure that relates the number of inhabitants to the territorial area of the municipality.
It is used to evaluate the degree of population concentration in a given region, typically
expressed in inhabitants per square kilometer (hab/km²). viii) Economically Active
Population (VPEA): Refers to the portion of the population in a given region or country
that is available for work. It includes all individuals who are employed or unemployed
but actively seeking work. ix) Child Labor (VTRINF): The percentage of children under
10 years old who are employed.
These variables will be used to better understand the income differentials between the
municipalities of Acre, taking into account a range of socio-economic factors such as
education, health, infrastructure, public administration, and demographic characteristics
like population density and the participation of the economically active population.
4. Results and Discussion
4.1. Regional Economy Indicators
Relative Participation
Acre's industrial profile is marked by a predominance of less dynamic sectors and a
tendency toward spatial concentration. The primary sectors of the state include
construction, which accounts for 54.6% of the industrial GDP, followed by food at 19.1%,
industrial utilities at 16.4%, wood at 3.2%, and non-metallic minerals at 2.2%. Together,
these sectors comprise 95.5% of the state's industrial output. Notably, the food sector has
seen the most significant increase in participation, rising by 10.1 percentage points
between 2009 and 2019.
When examining the relative participation of the workforce in each microregion, we find
that at least two major sectors - trade and services - have a substantial presence in the
economy. The microregions of Cruzeiro do Sul, Tarauacá, Sena Madureira, and Rio
Branco exhibit a high concentration of relative participation in the service sector. Between
2015 and 2020, even amidst the pandemic, these microregions experienced growth rates
of 4.5%, 5.7%, 11.94%, and 1.3%, respectively.
Brasiléia, Rio Branco, and Tarauacá have shown significant increases in their relative
participation in the trade sector. Both trade and services represent the core of Acre's
economy, and they account for a substantial portion of the occupied workforce.
However, it's important to note that the growth in industrial participation in Sena
Madureira outpaced that of Brasiléia, with Sena Madureira experiencing an increase of
118.4%, compared to Brasiléia 6.25%, despite Brasiléia having higher participation rates
in both 2015 and 2020. Conversely, the relative participation of civil construction
declined across all microregions, except for Rio Branco, which saw minimal growth. This
is noteworthy as Rio Branco, the state capital, has undergone more intense verticalization
and urbanization in recent years.
Table 02: Relative Participation of the Productive Structure by Microregions of Acre -
2015/2020
Large Sectors
2015
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.037
0.053
0.038
0.064
0.144
Construction
0.042
0.016
0.019
0.044
0.003
Trade
0.260
0.213
0.211
0.174
0.298
Services
0.642
0.666
0.561
0.693
0.509
Agricultural
0.019
0.053
0.171
0.024
0.045
Total
1.000
1.000
1.000
1.000
1.000
Large Sectors
2020
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.046
0.034
0.083
0.052
0.153
Construction
0.020
0.011
0.011
0.045
0.002
Trade
0.253
0.214
0.192
0.178
0.322
Services
0.671
0.704
0.628
0.702
0.481
Agricultural
0.010
0.037
0.086
0.023
0.043
Total
1.000
1.000
1.000
1.000
1.000
Source: own elaboration from RAIS/MTE data.
In comparing the years, it is evident that agriculture, civil construction, and industry faced
negative impacts throughout Acre during the COVID-19 pandemic. The microregion of
Cruzeiro do Sul was the most affected, as no sector experienced growth, with the sole
exception of the service sector. Conversely, trade and services emerged as the sectors that
grew the most in the economy, demonstrating resilience despite the challenging
circumstances.
Location Measurements
The first calculated indicator for location measurements is the Location Quotient (QL).
This indicator reveals which sectors are most specialized in certain regions compared to
a reference macro-region (AC). A QL greater than one indicates that the region is
specialized in that sector (Myrdal, 1968; Perroux, 1955). Despite the challenges posed by
the pandemic in 2020, the microregions of Tarauacá, Brasiléia, and Sena Madureira
exhibited strong specialization in three of the five main sectors of the economy. Brasiléia
and Sena Madureira were particularly notable in industry, trade, and agriculture, while
Tarauacá specialized in trade, services, and agriculture.
Furthermore, there was a marked specialization of the trade sector in the microregions of
Cruzeiro do Sul, Tarauacá, Sena Madureira, and Brasiléia. However, in 2020, there was a
significant decline in specialization within the construction industry across all
microregions, except for Rio Branco. Notably, Cruzeiro do Sul ceased to be specialized
in civil construction, indicating a shift towards spatial concentration in the microregion
of Rio Branco that emerged in response to the pandemic.
Table 03: Locational Quotient of the Productive Structure by Microregions of Acre -
2015/2020
Large Sectors
2015
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.587
0.828
0.591
1.008
2.253
Construction
1.013
0.378
0.452
1.074
0.084
Trade
1.388
1.134
1.126
0.929
1.591
Services
0.945
0.980
0.826
1.021
0.750
Agricultural
0.653
1.881
6.043
0.831
1.591
Large Sectors
2020
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.836
0.617
1.495
0.933
2.751
Construction
0.500
0.286
0.272
1.154
0.049
Trade
1.319
1.115
1.000
0.926
1.679
Services
0.974
1.022
0.913
1.019
0.698
Agricultural
0.406
1.476
3.458
0.937
1.708
Source: own elaboration from RAIS/MTE data.
The Acre Valley stands out for its strong participation in the agricultural and livestock
sectors, showcasing significant modernization in its productive structure. This
advancement has elevated the region to a higher stage of development compared to the
Juruá Valley. The dynamic presence of agriculture and livestock in the Acre Valley not
only drives the local economy but also fosters technological innovations and sustainable
practices, establishing the region as a hub for agro-industrial development. This
modernization positively influences economic indicators and has substantial implications
for the quality of life of the population, contributing to job creation, infrastructure
improvement, and the strengthening of the regional economic base.
Another important measure of location is the locational coefficient, which highlights the
influence of specific sectors on formal employment in each microregion relative to the
total labor force in Acre. This metric helps identify the degree of relative dispersion of
economic activities and selects those that show less tendency toward spatial
concentration.
In the period from 2015 to 2020, several notable results emerged. Firstly, there was a
spatial concentration of the trade sector in the microregions of Cruzeiro do Sul and
Brasiléia, with Brasiléia showing a greater trend of concentration compared to 2015.
Notably, civil construction emerged as a highlight in this context; while other sectors
experienced a decline in relative participation and specialization, civil construction
demonstrated an increased tendency for spatial concentration in the microregions of
Cruzeiro do Sul, Tarauacá, Sena Madureira, and Brasiléia. This suggests a positive
outlook for the creation of new formal jobs in this sector within these locations.
Table 04: Locational Coefficient of The Productive Structure by Microregion of Acre -
2015/2020
Large Sectors
2015
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.013
0.006
0.013
0.000
0.040
Construction
0.000
0.013
0.011
0.002
0.019
Trade
0.036
0.013
0.012
0.007
0.055
Services
0.019
0.007
0.059
0.007
0.085
Agricultural
0.005
0.013
0.072
0.002
0.008
Large Sectors
2020
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.005
0.011
0.014
0.002
0.049
Construction
0.010
0.014
0.014
0.003
0.019
Trade
0.031
0.011
0.000
0.007
0.065
Services
0.009
0.008
0.030
0.007
0.104
Agricultural
0.007
0.006
0.031
0.001
0.009
Source: own elaboration from RAIS/MTE data.
The Herfindahl-Hirschman Index (HHI) is used to assess the concentration of specific
sectors within microregions compared to the overall economy of Acre. This analysis
reveals a tendency for concentration in the microregions of Tarauacá and Brasiléia, which
show greater prominence and a stronger power of attraction due to their specialization in
three of the five key sectors. In Tarauacá, the highlighted sectors are trade, services, and
agriculture, while Brasiléia excels in industry, commerce, and agriculture.
In 2020, there was a notable concentration of the trade sector in the microregions of
Cruzeiro do Sul, Tarauacá, and Brasiléia, as well as in agriculture in Tarauacá, Sena
Madureira, and Brasiléia. However, civil construction experienced a decline in its
attractiveness across all microregions, except for Rio Branco. This trend indicates a
development process that is polarized by microregion, aligning with Perroux's theory
(1977).
Furthermore, it can be inferred that the pandemic spurred growth in the trade and services
sectors, contributing to an internalization of the economy. In contrast, the industrial sector
experienced a decrease in its attraction power in Rio Branco while gaining momentum in
the microregions of Brasiléia and Sena Madureira. Meanwhile, construction remains
concentrated in Rio Branco, which accounts for a significant portion of formal
employment compared to other regions in the state.
Table 05: Hirschman Herfindahl Index of the Productive Structure by Microregion of
Acre - 2015/2020
Large Sectors
2015
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
-0.034
-0.005
-0.010
0.007
0.042
Construction
0.001
-0.018
-0.013
0.062
-0.031
Trade
0.032
0.004
0.003
-0.059
0.020
Services
-0.005
-0.001
-0.004
0.018
-0.008
Agricultural
-0.029
0.026
0.123
-0.140
0.020
Large Sectors
2020
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
-0.015
-0.012
0.013
-0.054
0.069
Construction
-0.046
-0.023
-0.018
0.125
-0.038
Trade
0.029
0.004
0.000
-0.060
0.027
Services
-0.002
0.001
-0.002
0.016
-0.012
Agricultural
-0.055
0.015
0.062
-0.051
0.028
Source: own elaboration from RAIS/MTE data.
Specialization Measures
This study utilized two indicators for measuring specialization: the Specialization
Coefficient and the Restructuring Index (Alves et., 2019). The Specialization Coefficient
compares the productive structure of a microregion with that of the entire state of Acre.
A microregion that exhibits a more differentiated productive structure relative to the state
is considered specialized in certain sectors.
In 2015, there was an increase in production specialization within the industry and
construction sectors across the microregions of Tarauacá, Sena Madureira, Rio Branco,
and Brasiléia. These specializations proved significant for the state. Notably, Cruzeiro do
Sul did not achieve specialization in the industrial sector; however, it did experience an
increase in productive specialization in civil construction.
It is also important to highlight the productive specialization of the microregion of
Brasiléia across all sectors. This area comprises four municipalities - Brasiléia, Xapuri,
Epitaciolândia, and Assis Brasil- each with populations ranging from 10,000 to 30,000
inhabitants. These municipalities share common characteristics, including the presence
of a natural reserve within their boundaries.
Table 06: Coefficient of Specialization of the Productive Structure by Microregion of
Acre - 2015/2020
Large Sectors
2015
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.017
0.003
0.005
0.003
0.021
Construction
0.001
0.009
0.007
0.031
0.015
Trade
0.016
0.002
0.002
0.030
0.010
Services
0.002
0.000
0.002
0.009
0.004
Agricultural
0.014
0.013
0.061
0.070
0.010
Large Sectors
2020
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.008
0.006
0.006
0.027
0.035
Construction
0.023
0.011
0.009
0.062
0.019
Trade
0.015
0.002
0.000
0.030
0.013
Services
0.001
0.000
0.001
0.008
0.006
Agricultural
0.027
0.008
0.031
0.025
0.014
Source: own elaboration from RAIS/MTE data.
Confirming the dynamism of the microregions of Rio Branco and Brasiléia across all
sectors, industry remains strong despite a reduction in specialization for services and
agriculture in Rio Branco. Brasiléia stands out by concentrating companies with
significant potential, contributing to a large share of the state’s GDP and a greater number
of formal employees. In contrast, Rio Branco is characterized by its denser population,
including the capital, and has historically benefitted from a higher level of
industrialization. However, the trade, services, and agriculture sectors in the microregions
of Tarauacá, Sena Madureira, and Rio Branco did not achieve specialization and, in some
cases, even experienced declines relative to the state.
The coefficient for restructuring the productive structure indicates changes between 2015
and 2020, suggesting that certain microregions began to specialize in specific sectors in
response to the economic conditions in Acre. Notably, the microregions of Rio Branco
and Cruzeiro do Sul began to distinguish themselves from others in terms of civil
construction, indicating a more advanced development in this sector. While Rio Branco
experienced an increase in relative participation and job creation, Cruzeiro do Sul saw a
lower intensity of growth in these areas.
Table 07: Restructuring Coefficient of the Productive Structure by Microregion of Acre
- 2015/2020
Large Sectors
2015/2020
Cruzeiro do Sul
Tarauacá
Sena Madureira
Rio Branco
Brasiléia
Industry
0.014
0.002
0.012
0.040
0.016
Construction
0.019
0.001
0.002
0.022
0.000
Trade
0.003
0.001
0.001
0.010
0.006
Services
0.006
0.002
0.001
0.010
0.001
Agricultural
0.008
0.004
0.030
0.035
0.007
Source: own elaboration from RAIS/MTE data.
The verticalization process in certain cities makes them more attractive for large
companies to establish operations. For example, Cruzeiro do Sul in its respective
microregion, as well as Rio Branco and its surrounding municipalities, benefit from this
trend. Given their locations and the polarization of growth and development in the state,
there is a noticeable flow of productive activities toward these two microregions.
However, the changes in the productive structure of Sena Madureira, particularly in the
agricultural and industrial sectors, are significant, alongside the industrial developments
in Brasiléia, highlighting the productive restructuring occurring in Acre during this
period.
Regarding the Juruá Valley and its municipalities, this mesoregion continues to exhibit a
significant history of geographical isolation, particularly in the cases of Marechal
Thaumaturgo, Porto Walter, and Jordão, where access is limited to waterways and/or
airstrips. This distinctive geographical condition has led to a scenario where, for an
extended period - at least until the early 2000s - the commercial and economic ties of
Cruzeiro do Sul were more closely aligned with Manaus (via waterways) than with the
capital, Rio Branco. This dynamic arose from the limited accessibility of the BR-364,
which connects the state to the rest of the country.
This situation has notably shaped the developmental patterns of these two significant
regions, establishing substantial disparities. The Juruá Valley, due to its geographical
isolation, has faced unique challenges regarding connectivity and accessibility, directly
impacting its economic and commercial interactions. In contrast, Cruzeiro do Sul has
benefited from stronger connections with Manaus, resulting in a development trajectory
influenced by distinct regional dynamics. This contrast underscores the complexity of
geographical and logistical interactions in shaping the regional socio-economic
landscape.
4.2. Exploratory Analysis of Spatial and Model Data
Conducting a spatial analysis is essential for understanding inequalities across the
territory, as it provides insight into the location of less developed regions in relation to
more developed ones and how to implement targeted development strategies. The
Exploratory Spatial Analysis (ESA) method describes the distribution and spatial
association of specific variables among the evaluated units (municipalities), allowing us
to discern patterns of spatial instability and identify outliers. Clusters are classified into
four categories: High-High, Low-Low, High-Low, and Low-High.
In this initial analysis using a univariate context, the goal is to identify spatial patterns
and evidence significant clusters for the Local Moran’s I. When examining cluster maps
for 2000 and 2010, a notable expansion of High-High agglomerations is evident,
particularly in the vicinity of the capital, Rio Branco. This indicates a concentration of
per capita income in municipalities within the Rio Branco region and the western part of
the state. Inequalities between the eastern and western regions are apparent, showing that
income concentration around Rio Branco diverges from other municipalities, such as
Tarauacá and Marechal Thaumaturgo, which reported per capita incomes below the state
average in 2000.
Although Cruzeiro do Sul is one of the central cities, it ranks as High-Low in per capita
income, as observed in 2010. Additionally, Jordão and Marechal Thaumaturgo were
classified as Low-Low municipalities, characterized by low per capita incomes and
similar profiles. It is important to note that outliers, such as Rio Branco, are not significant
in this analysis.
Figure 02: Single-rate LISA cluster map for per capita income variable - 2000 and 2010
a) 2000
b) 2010
Source: Own elaboration from the results.
Figure 03 presents the reverse side, illustrating the differences between the average per
capita income of the state and that of its municipalities, thereby revealing per capita
income differentials. The High-High clusters that transitioned to Low-Low, as well as the
Low-Low clusters that became High-High, highlight inter-municipal inequalities. This
pattern indicates that areas of concern regarding regional development are concentrated
in the western part of the state, which is the westernmost region of Brazil. Overall, the
analysis identifies weaknesses in driving forces that contribute to the creation and
expansion of inter-municipal inequalities in Acre
2
.
Figure 03: Single-rate LISA cluster map for per capita income differentials variable -
2000 and 2010
2
The propulsive effects produce positive aspects to the development of the most distant regions. Among the aspects,
there is the reduction of unemployment, the transfer of technological progress and the increase in commercial
transactions in these regions.
a) 2000
b) 2010
Source: Own elaboration from the results.
The clusters identified in the analysis support the hypothesis guiding this study. They
highlight that spatial factors significantly explain intermunicipal inequalities in Acre,
revealing a high concentration of per capita income differentials in the western
municipalities of the state. This concentration underscores the region's lag in productivity
structure, despite the economic growth observed in Cruzeiro do Sul. Consequently, these
regions contribute to perpetuating income inequalities across Acre in the early 21st
century.
In the bivariate analysis, the dependent variable—per capita income differentials -was
examined alongside three key variables: (A) Infant Mortality Rate (VMINF5), (B) Low-
Income Population (VPBRp), and (C) Education (VEDUC). This approach is justified by
the importance of health, education, and income indicators in assessing municipal human
development. Ideally, municipalities should exhibit low infant mortality rates, higher
average years of schooling, and a reduced proportion of the population with low per capita
income.
Figure 04: Bivariate LISA Cluster Map - 2000 (Variables Selected)
(A) VDRENPC x VMINF5
(B) VDRENPC x VPBRp
(C) VDRENPC x VEDUC
Source: Own elaboration from the results.
The bivariate Moran's I statistics reveal that municipalities with high per capita income
differentials and elevated infant mortality rates-indicative of poorer health and poverty
conditions-are predominantly found in the microregions of Tarauacá and Cruzeiro do Sul
in the western part of the state. Conversely, areas with low-income differentials and better
health indicators are concentrated around Rio Branco, Plácido de Castro, and Xapuri.
Regarding education, there is an inverse relationship: as per capita income differentials
increase, educational levels tend to decline. Notably, the municipalities of Xapuri and
Bujari exhibit higher educational levels, forming low-high clusters.
By 2010, only minor changes occurred, maintaining positive spatial correlations between
per capita income differentials and both the infant mortality rate and the low-income
population. In terms of education, a new High-High cluster emerged in Rodrigues Alves,
characterized by a significant per capita income differential (indicating lower income)
alongside a higher educational attainment, with an average of 8.5 years of schooling
compared to Acre's overall average of 7.75 years.
Figure 05: Bivariate LISA Cluster Map - 2010 (Selected Variables)
(A) VDRENPC x VMINF5
(B) VDRENPC x VPBRp
(C) VDRENPC x VEDUC
Source: Own elaboration from the results.
4.3. Intermunicipal Income Inequalities in Acre
This analysis aims to provide insights into the determinants of inter-municipal income
inequality, taking spatial effects into account. Previous analyses have indicated that
unobserved effects are significant for understanding this phenomenon and that spatial
factors should be considered. In this context, we pose two key questions: Is the spatial
factor relevant for explaining inter-municipal income inequality? What are its impacts?
To address these questions, it is essential to understand the statistics, estimates, and
strategies employed to generate the results presented in Table 08, which refers to the
Regression Model with Fixed Effects Panel Data and Spatial Dependence (SAR Model).
For the Exploratory Spatial Analysis (ESA) conducted earlier, a neighborhood matrix
based on the two nearest neighbors’ criterion was utilized. This means that for a given
municipality X, we considered its two closest neighboring municipalities relative to its
centroid. The choice of this neighborhood matrix was informed by testing the regression
residuals (Murphy et., 1989).
Initially, the regression was performed without considering spatial effects and was
estimated using Ordinary Least Squares (OLS). The dependent variable, per capita
income differentials in municipalities (VDRENPC), was analyzed alongside several
independent variables: Gini coefficient, mean years of schooling, infant mortality rate (up
to five years of age), water supply access, percentage of the population with income below
half the minimum wage, gross added value from the administration sector (VABAdm),
child labor, economically active population, and population density. Following this, we
evaluated the regression residuals to identify spatial autocorrelation using Moran's I.
To determine the most appropriate model based on the neighborhood matrix, we
conducted the Lagrange multiplier diagnostic test for spatial dependence. This test assists
in identifying the best-suited model for the dataset and spatial weights. The estimated
model exhibited non-normality in the residuals. Based on the results from both the normal
and robust Lagrange Multiplier tests for spatial dependence, we concluded that the SAR
model is the most appropriate for estimation and interpretation.
According to Silva (2019), additional robustness and adequacy tests were performed,
including the Spatial Hausman test. This test helps to determine the most suitable choice
between the fixed effects model and the random effects model. In this case, the Hausman
test for models with spatial dependence indicated, at a 1% significance level, that the
fixed effects model is preferred, leading to the rejection of the null hypothesis favoring
random effects.
We also identified issues of spatial heterogeneity, which are linked to Goodchild's (2004)
second Law of Geography. This phenomenon occurs when structural instability manifests
across regions, resulting in varied responses based on location or spatial scale, often
characterized by variable coefficients or spatial regimes (Almeida, 2012). Table 08
presents the key results derived from the econometric-spatial estimation.
Table 08: Regression Model with Fixed Effects Panel Data with Spatial Dependency -
SAR Model and Impacts Generated
3
Coefficients
Regression
Fixed effects
Impacts
Direct
Indirect
Total
VTHEIL
-41.6757***
(12.3664)
-48.4885***
(15.8176)
-42.1604**
(20.0451)
-90.6489***
(34.1861)
VEDUC
5.6750*
(2.7307)
6.6027**
(3.1881)
5.7410*
(3.3970)
12.3437*
(6.4099)
VMINF5
-5.4798***
(1.0988)
-6.3755***
(1.4357)
-5.5435**
(2.2619)
-11.9190***
(3.5108)
VAA
0.0237**
(0.0079)
0.02763**
(0.0099)
0.0240**
(0.0120)
0.0516**
(0.0210)
VEE
1.5288***
1.7787***
1.5465***
3.3252***
3
It is noticed that the coefficient of spatial disturbance “ρ” was controlled, that is, it was not statistically significant,
therefore, the model controlled the spatial effects.
(0.2134)
(0.2717)
(0.5488)
(0.7681)
VPBRp
6.9809***
(0.4690)
8.1221***
(0.7251)
7.0621***
(2.3365)
15.1841***
(2.9388)
LVABAdm
17.6967*
(7.6967)
20.5896**
(9.2997)
17.9025*
(10.1783)
38.4921**
(18.8296)
VDENSPOP
-24.1835***
(2.9905)
-28.1369***
(3.8254)
-24.4648***
(8.5397)
-52.6017***
(11.6208)
VPEA
-0.0117*
(0.0049)
-0.0136**
(0.0061)
-0.0118*
(0.0069)
-0.0254**
(0.0126)
VTRINF
0.0932
(0.3719)
0.1084
(0.4754)
0.0942
(0.4606)
0.2026
(0.9295)
Spatial
autoregressive
coefficient
Estimation
Standard deviation
t-value
Pr (>|t|)
ρ
0.5403
0.0729
7.4104
0.1259
Source: own elaboration from the results
The analysis of the Theil index coefficient, a statistical measure of income distribution,
reveals a negative result. This outcome indicates that an increase in the Theil index will
harm the per capita income of municipalities already below the state average, thereby
exacerbating inequalities between them. Specifically, any increase in the Theil index
within a municipality leads to a direct reduction of R$ 48.49 in its per capita income,
while the neighboring municipality experiences an indirect reduction of R$ 42.16.
Altogether, this poor income distribution results in a total reduction of R$ 90.65 in per
capita income.
The proxy coefficient for schooling indicates a positive correlation, meaning that higher
education levels are associated with increased per capita income, thus narrowing income
disparities in Acre. Municipalities with more years of schooling tend to experience greater
economic growth than the state average. For example, each additional year of average
education boosts per capita income by R$ 6.60 directly and R$ 5.74 indirectly, leading to
a total effect of R$ 12.34. This may also reflect the migration of youth to other
municipalities in search of better educational opportunities.
The coefficient for the infant mortality rate shows a negative correlation, indicating that
poorer health conditions hinder income growth. Municipalities with higher infant
mortality rates generally exhibit lower per capita income compared to those with better
health indicators. Specifically, a 1% increase in infant mortality results in a direct
reduction of approximately R$ 6.38 in per capita income, along with an indirect impact
of R$ 5.54 from neighboring municipalities, totaling a reduction of R$ 11.92.
The water supply coefficient demonstrates a positive relationship, suggesting that
improvements in water access correlate with higher per capita income. Each improvement
generates a direct increase of R$ 0.03 in the municipality, with an indirect benefit of R$
0.02 for neighboring areas, leading to a total effect of R$ 0.05. However, the overall
impact on per capita income remains minimal due to the specific characteristics of the
Amazon hydrographic basin where Acre is located.
A positive relationship exists between adequate electricity supply and per capita income,
confirming that municipalities with better electricity access tend to have higher incomes.
This relationship results in a direct impact of R$ 1.78 and an indirect effect of R$ 1.55,
culminating in a total effect of R$ 3.33.
Conversely, a higher percentage of the population earning below half the minimum wage
correlates with increased income inequality. Municipalities with more low-income
residents tend to widen the income gap relative to the state average. This situation results
in a direct impact of R$ 8.12 and an indirect spillover effect of R$ 7.06 from neighboring
municipalities, totaling R$ 15.18.
The Gross Added Value (VAB) of the administration sector positively influences per
capita income differentials. Municipalities with higher VAB tend to enjoy greater income.
An increase of 1% in VAB generates a direct effect of R$ 20.60 and an indirect effect of
R$ 17.90, leading to a total reduction in income disparity of R$ 38.50.
Population density exhibits a negative and statistically significant relationship with per
capita income differentials. Densely populated areas often incur higher costs, which
hinder income growth compared to the state average. A 1% increase in population density
results in a direct reduction of R$ 28.14 and an indirect decrease of R$ 24.47, culminating
in a total reduction of approximately R$ 52.60 in per capita income.
Finally, the Economically Active Population variable shows a negative correlation with
per capita income differentials. This suggests that an increase in job seekers can lead to
lower wages due to a higher labor supply, negatively impacting per capita income. A 1%
increase in the economically active population leads to a direct reduction of R$ 0.01 and
an indirect reduction of R$ 0.01, totaling approximately R$ 0.02. No significant
relationship exists between child labor and per capita income differentials.
5. Conclusion
In general terms, the analyses suggest that the Microregion of Rio Branco has the most
advanced productive structure in the state. This microregion employs the largest
workforce and exhibits the highest demographic density. The strong economic
dependence results from significant structural advantages, including the presence of the
state capital and its connections to Manaus and Porto Velho, which foster greater
economic dynamics.
Acre's industrial profile primarily consists of less dynamic sectors and tends to
concentrate spatially. The main sectors of the state include construction, accounting for
54.6% of the industrial GDP, followed by food at 19.1%, industrial utilities at 16.4%,
timber at 3.2%, and non-metallic minerals at 2.2%. Together, these sectors make up 95.5%
of the state's industrial output. Notably, the food sector increased its participation
significantly, rising by 10.1 percentage points between 2009 and 2019.
The trade and services sectors show remarkable dynamics, with the microregions of
Brasiléia, Rio Branco, and Tarauacá experiencing significant growth in trade. The service
sector has also expanded in Sena Madureira, Tarauacá, and Rio Branco, both sectors
acting as the pillars of Acre's economy. Despite the challenges posed by the COVID-19
pandemic in 2020, Tarauacá, Brasiléia, and Sena Madureira specialized in three of the
five main economic sectors—industry, trade, and agriculture—while Tarauacá focused on
trade, services, and agriculture. However, the agricultural sector suffered the most during
the pandemic, with reduced participation across all microregions, followed by declines in
civil construction and industry.
The coefficient for restructuring the productive structure indicates that the microregions
of Rio Branco and Cruzeiro do Sul are increasingly differentiating themselves in civil
construction, suggesting a more advanced status in these areas. This inequality is
particularly pronounced in western Acre, where polarization dynamics have diminished.
The central part of the state does not exhibit high or low per capita income, likely due to
low population density. While residents seek better living conditions, the Juruá Valley
mesoregion continues to host a significant number of municipalities with low per capita
income, reflecting underdevelopment in the region.
Two notable aspects deserve attention: 1) The Acre Valley has a strong presence in the
agricultural and livestock sectors, showcasing a robust modernization of its productive
infrastructure, placing it at a more advanced developmental stage than the Juruá Valley;
2) The municipalities in the Juruá Valley, particularly Marechal Thaumaturgo, Porto
Walter, and Jordão, remain geographically isolated, with access limited to waterways and
airstrips. Until the early 2000s, Cruzeiro do Sul’s commercial and economic interactions
aligned more closely with Manaus than with the capital, Rio Branco, due to the limited
accessibility of BR-364, which connects the state to the rest of the country. Consequently,
the developmental patterns in these two major regions have diverged significantly.
Overall, spatial factors indicate a concentration of high per capita income in two
municipalities of the Acre Valley, while two municipalities with low income and one with
high income were identified in the microregions of Tarauacá and Cruzeiro do Sul in 2010.
This situation perpetuates regional inequalities in Acre during the first decade of the 21st
century.
Regarding inter-municipal income inequality, reducing disparities goes beyond merely
decreasing per capita income differentials. Improvements in socioeconomic indicators
such as income distribution, health, and infrastructure—particularly in municipalities
adjacent to Cruzeiro do Sul—are likely to enhance economic growth and further affect
inter-municipal income differences in Acre.
The spatial factor plays a crucial role in explaining regional inequalities, especially in
municipalities with a history of geographic isolation. In locations like Marechal
Thaumaturgo, Porto Walter, and Jordão, where access is limited to waterways and
airstrips, the lack of connectivity creates significant disparities compared to more
accessible areas.
The economic impact of geographical isolation manifests in various ways. Limited road
access hampers the transport of goods and inputs, negatively affecting local commerce.
Furthermore, inadequate connectivity deters investment and infrastructure development,
stunting economic growth in these regions.
These spatial inequalities also influence trade dynamics. For instance, Cruzeiro do Sul
historically maintained closer ties with Manaus than with the capital, Rio Branco, due to
accessibility issues surrounding BR-364. This situation illustrates how spatial conditions
shape patterns of economic development and trade interactions across the state.
Thus, government intervention through explicit and implicit policies is crucial for
reducing regional inequalities in Acre. The public sector plays an essential role in the
economies of smaller municipalities, highlighting the need for adequate electricity,
educational incentives, improved health conditions, and a better understanding of local
economies and their spatial economic impacts. Strengthening public and welfare policies
is vital, as the study reveals that health, education, infrastructure, and other factors
significantly influence per capita income differentials between municipalities. Addressing
these determinants is critical in combating inter-municipal income inequality.
References
Almeida, E. (2012). Econometria espacial. Alínea.
Alves, D. F. (2020). Estrutura produtiva e desigualdade intermunicipal de renda no
Brasil: uma abordagem regional [Dissertação de mestrado, Universidade Federal do
Rio Grande do Norte].
Alves, D. F. (2023a). Estrutura produtiva e desigualdades intermunicipais de renda: Um
estudo de caso do Rio Grande do Norte, Brasil. Revista de Economía del Caribe, 31, 1.
Alves, D. F. (2023b). Economia do Estado de São Paulo: Estrutura econômica e
desigualdades intermunicipais de renda. Revista de Economia Regional, Urbana e do
Trabalho, 12(2), 60–94.
Alves, D. F., Do O’de Lima Júnior, F., & Pereira, W. E. N. (2019). Disparidades
locacionais na estrutura produtiva e fragmentação territorial: uma análise das
mesorregiões do Rio Grande do Norte. Revista Estudo & Debate, 26(3).
Anselin, L. (1988). Lagrange multiplier test diagnostics for spatial dependence and
spatial heterogeneity. Geographical Analysis, 20(1), 1–17.
Castro, V. C., Kuhn, L., & Pena, H. W. A. (2017). Análise do quociente locacional e
da dinâmica produtiva do município de Salinópolis – Pará. Revista Observatório de la
Economia Latino-americana, Brasil, setembro.
Faggio, G., & Overman, H. (2014). The effect of public sector employment on local
labor markets. Journal of Urban Economics, 79, 91–107.
Fernandes, C. N., & Bezerra, F. D. da S. (2019). Desenvolvimento social na
Amazônia Ocidental: uma análise a partir dos municípios do estado Acre. Economia e
Desenvolvimento, 31, e7–e7.
Fishlow, A. (1973). Brazil's economic miracle. The World Today, 29(11), 474–481.
Goodchild, M. (2004). The validity and usefulness of laws in geographic information
science and geography. Annals of the Association of American Geographers, 94(2),
300–303.
Haddad, J. H. (Org.). (1989). Economia regional: teoria e métodos de análise.
BNB/ETIENE.
Lima, M. J. de. (1994). Capitalismo e extrativismo: a formação da região acreana
[Tese de doutorado, Universidade Estadual de Campinas]. IE/UNICAMP.
Lima, É. P. C., Silva, A. C., & Lima, E. C. (2017). Assimetrias locacionais e padrão
industrial: uma análise das mesorregiões do Rio Grande do Norte. Revista Espacios, 38,
23–35.
Martinello, P. (1988). A “Batalha da Borracha” na Segunda Guerra Mundial e suas
consequências para o Vale Amazônico. Cadernos UFAC – Série “C” Estudos e
Pesquisas. UFAC.
Murphy, K. M., Shleifer, A., & Vishny, R. W. (1989). Industrialization, and the big
push. Journal of Political Economy, 97(5), 1003–1026.
Myrdal, G. (1968). Teoria econômica e regiões subdesenvolvidas (2. ed.). SAGA.
Neri, M. (2010). Distribuição de renda: evolução recente. Centro de Políticas Sociais –
FGV.
http://www.fgv.br/cps/pesquisas/Politicas_sociais_alunos/2010/BES_DistrRenda_EvoR
ecente_PME.pdf
Neves, C., et al. (2015). Concentração de renda nos municípios da região Sul entre
2000 e 2010: análise geográfica temporal utilizando a abordagem exploratória de dados
espaciais. Revista Espacios, 36(23).
Perroux, F. (1955). Notes sur la notion de pôle de croissance. Economie Appliquée, 8,
307–322.
Pessôa, S. A. (1997). Existe um problema de desigualdade regional no Brasil? Revista
Econômica do Nordeste, 28(4).
Piacenti, C. A., & Lima, J. F. (Orgs.). (2017). Métodos de análise regional.
Rocha, C. B., & Parré, J. L. (2009). Estudo da distribuição espacial do setor
agropecuário do Rio Grande do Sul. Análise Econômica, 27(52).
Silva, B. M., et al. (2010). Desequilíbrios regionais no Brasil e a hipótese da
convergência do desenvolvimento. RDE - Revista de Desenvolvimento Econômico,
12(22), 5–22.
Silva, J. L. M. (2019). Migração intermunicipal no Brasil: evidências para as décadas
de 2000 e 2010 [Dissertação de mestrado, Universidade Federal do Rio Grande do
Norte].
Simões, R. F., et al. (2005). Métodos de análise regional e urbana: diagnóstico
aplicado ao planejamento. Cedeplar, Universidade Federal de Minas Gerais.
Souza, C. A. A. (1992). História do Acre. M. M. Paim.
